Practical Hydroinformatics - gebonden uitgave, pocketboek

EAN (ISBN-13): 9783540798804
ISBN (ISBN-10): 3540798803
Gebonden uitgave
Verschijningsjaar: 2008
Uitgever: Springer Berlin
506 Bladzijden
Gewicht: 1,022 kg
Taal: eng/Englisch

Boek bevindt zich in het datenbestand sinds 2008-01-15T00:12:41+01:00 (Amsterdam)
Detailpagina laatst gewijzigd op 2023-10-19T14:34:38+02:00 (Amsterdam)
ISBN/EAN: 3540798803

ISBN - alternatieve schrijfwijzen:
3-540-79880-3, 978-3-540-79880-4
alternatieve schrijfwijzen en verwante zoekwoorden:
Auteur van het boek: see, dimitri
Titel van het boek: computational intelligence, science water, deve, practical vim, dimitri

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Gegevens van de uitgever

Auteur: Robert J. Abrahart; Linda M. See; Dimitri P. Solomatine
Titel: Water Science and Technology Library; Practical Hydroinformatics - Computational Intelligence and Technological Developments in Water Applications
Uitgeverij: Springer; Springer Berlin
506 Bladzijden
Verschijningsjaar: 2008-11-04
Berlin; Heidelberg; DE
Taal: Engels
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
Available
XVI, 506 p. 243 illus., 5 illus. in color.

BB; Hardcover, Softcover / Geowissenschaften/Geologie; Geologie und die Lithosphäre; Verstehen; Geowissenschaften; Fuzzy; Groundwater; algorithms; computational intelligence; decision support system; digital elevation model; hydroinformatics; hydrology; intelligence; learning; machine learning; optimization; soft computing; uncertainty; water resources; hydrogeology; Geology; Water; Theory of Computation; Artificial Intelligence; Complex Systems; Earth Sciences; Hydrologie und die Hydrosphäre; Theoretische Informatik; Künstliche Intelligenz; Kybernetik und Systemtheorie; Geowissenschaften; EA; BC; BA

Hydroinformatics: Integrating Data and Models.- Some Future Prospects in Hydroinformatics.- Data-Driven Modelling: Concepts, Approaches and Experiences.- Artificial Neural Network Models.- Neural Network Hydroinformatics: Maintaining Scientific Rigour.- Neural Network Solutions to Flood Estimation at Ungauged Sites.- Rainfall-Runoff Modelling: Integrating Available Data and Modern Techniques.- Dynamic Neural Networks for Nonstationary Hydrological Time Series Modeling.- Visualisation of Hidden Neuron Behaviour in a Neural Network Rainfall-Runoff Model.- Correction of Timing Errors of Artificial Neural Network Rainfall-Runoff Models.- Data-Driven Streamflow Simulation: The Influence of Exogenous Variables and Temporal Resolution.- Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks.- Neural Network Estimation of Suspended Sediment: Potential Pitfalls and Future Directions.- Models Based on Fuzzy Logic.- Fuzzy Logic-Based Approaches in Water Resource System Modelling.- Fuzzy Rule-Based Flood Forecasting.- Development of Rainfall–Runoff Models Using Mamdani-Type Fuzzy Inference Systems.- Using an Adaptive Neuro-fuzzy Inference System in the Development of a Real-Time Expert System for Flood Forecasting.- Building Decision Support Systems based on Fuzzy Inference.- Global and Evolutionary Optimization.- Global and Evolutionary Optimization for Water Management Problems.- Conditional Estimation of Distributed Hydraulic Conductivity in Groundwater Inverse Modeling: Indicator-Generalized Parameterization and Natural Neighbors.- Fitting Hydrological Models on Multiple Responses Using the Multiobjective Evolutionary Annealing-Simplex Approach.- Evolutionary-based Meta-modelling: The Relevance of Using Approximate Models in Hydroinformatics.- Hydrologic Model Calibration Using Evolutionary Optimisation.- Randomised Search Optimisation Algorithms and Their Application in the Rehabilitation of Urban Drainage Systems.- Neural Network Hydrological Modelling:An Evolutionary Approach.- Emerging Technologies.- Combining Machine Learning and Domain Knowledge in Modular Modelling.- Precipitation Interception Modelling Using Machine Learning Methods – The Dragonja River Basin Case Study.- Real-Time Flood Stage Forecasting Using Support Vector Regression.- Learning Bayesian Networks from Deterministic Rainfall–Runoff Models and Monte Carlo Simulation.- Toward Bridging the Gap Between Data-Driven and Mechanistic Models: Cluster-Based Neural Networks for Hydrologic Processes.- Applications of Soft Computing to Environmental Hydroinformatics with Emphasis on Ecohydraulics Modelling.- Data-Driven Models for Projecting Ocean Temperature Profile from Sea Surface Temperature.- Model Integration.- Uncertainty Propagation in Ensemble Rainfall Prediction Systems used for Operational Real-Time Flood Forecasting.- OpenMI – Real Progress Towards Integrated Modelling.- Hydroinformatics – The Challenge for Curriculum and Research, and the “Social Calibration”of Models.- A New Systems Approach to Flood Management in the Yangtze River, China.- Open Model Integration in Flood Forecasting.
Includes supplementary material: sn.pub/extras